Research And Application Of Event Extraction Method Based On Improved Multi-layer Label Pointer-net

With the explosive development of the Internet,the Internet has become an important platform for people’s daily communication,sharing,dissemination,and access to information.Massive amounts of information and data are presented in the form of text,making people enjoy the convenience brought by the Internet while also being burdened.Troubled by information.Therefore,how to find the information that people care about from the massive text is a problem that needs to be solved urgently,and information extraction has become the focus of everyone’s research.Event extraction research is an important subtask in information extraction research,and it is also a difficult and hot point in information extraction tasks.The task of event extraction is to extract events that users are interested in from the text,and present them in a structured form.At present,event extraction has been widely used in fields such as intelligent question answering,knowledge graphs,information retrieval,recommendation systems,and automatic text summarization.Event extraction consists of two subtasks: event detection and event argument extraction.Traditional event detection identifies trigger words based on sequence tagging models,and the process mainly includes trigger word recognition and trigger word classification.This paper combines the attention mechanism and event label information,and proposes the EDLA algorithm,which uses the attention mechanism to automatically capture the event information contained in the text,does not depend on trigger words,and does not require artificial construction of features.At the same time,this method modeling the event tag information together effectively improves the model’s ability to understand tags.Event argument extraction is divided into argument identification and argument role classification.We propose an event argument extraction model named MP-QA that combines the advantage of machine reading comprehension-based model and multilayer label pointer-net-based model.Firstly,through the multi-layer label pointer-net,our model can effectively solve the problem of entity overlap and role overlap that cannot be solved by the traditional sequence-based event argument extraction model.Secondly,by modeling the event arguments of the same event together,we effectively solve the problem that the traditional machine reading comprehension-based model ignores the correlation between event arguments.Finally,by reducing the label matrix,the problem that the multi-layer label pointer network is difficult to converge is solved effectively.By comparing the DUEE dataset with other existing methods,the algorithm in this paper significantly improves the performance of event extraction.Finally,using the event extraction algorithm proposed in this paper,a target review application framework based on event extraction is constructed,which can effectively generate target tracking event lines and provide support for subsequent military decision-making.